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In this paper we investigate a situation where we want to perform a coherence analysis of two signal sources, one of which is measured directly, and the other is measured through a sensor array affected by noise. To extract the latter signal, we suggest the use of the optimal beamforming with reference. We note, however, that this approach results in a coherence estimate that is noticeably biased, and cannot be evaluated by the known statistical tests. We therefore derive a new statistical test, that allows the evaluation of the biased coherence estimate. We illustrate the applicability of our methodology on the coherence analysis of EEG and EMG signals. We note that the suggested approach has several advantages over the surface Laplacian, which is a spatial filter commonly used in the EEG-EMG coherence analysis., (© 2013 Elsevier Ltd. Published by Elsevier Ltd. All rights reserved.)

Objective: This paper aims to improve the shortcomings of the extant methodologies for realistic Laplacian (RL) computation, and correct the erroneous claims published in the past., Methods: We implemented several variants of RL computation methods, using various potential approximation techniques and different regularization approaches. The individual variants of the RL computation were tested using simulations based on a realistic head model computed with the boundary element method (BEM). The results which disagreed with previously published works were further analyzed, and the reasons for the disagreement were identified., Results: We identified the best regularization techniques for the surface potential approximation, and we showed that once these techniques are used there is often little difference between various potential approximations, which is in contrast with previous claims that promoted the radial basis function (RBF) approximation. Further, our analysis shows that the RBF approximation suffers from Runge phenomenon, which cannot be mitigated simultaneously for both deep and shallow sources; therefore, its good performance is guarantied only if a priori knowledge about the source depth is available., Conclusions: The previously published methodology for RL computation was not optimal. Improvements are possible if the newly suggested approach is used., Significance: The methodology presented in our paper allows more efficient utilization of the RL, providing a useful tool for processing of high density EEG recordings. Presented techniques allow to achieve high EEG spatial resolution, and avoid unnecessary spatial blurring caused by the problems in the previously published RL methodology., (Copyright © 2012 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.)

Effects of isometric muscle contraction on amplitude and coherence changes of EEG rhythms during repetitive cutaneous electrical stimulation were analyzed in 10 right-handed subjects. Subjects received electrical stimuli at intensity above pain threshold to their right middle finger while either squeezing a rubber tube with the right index finger and thumb, or keeping their ipsilateral hand muscles relaxed. EEG was recorded using 111 closely spaced electrodes. Somatosensory stimuli were followed by reduction of the relative 8-12 and 16-24 Hz band power (at 0.2-0.4 s) in bilateral primary sensorimotor cortices (S1/M1) and medial frontal cortex, and by a subsequent increase in 16-24 Hz band power (at 0.9 s). Isometric muscle contraction strongly suppressed these band power changes. The 8-12 and 16-24 Hz mean coherence in a wide region surrounding the contralateral S1/M1 and in the medial frontal cortex showed an initial decrease, partially paralleling band power changes, and later an increase. Ipsilateral S1/M1 showed a decrease in 8-12 Hz coupling only with the central and frontal electrodes of the same hemisphere. Muscle contraction reduced all coherence changes, but enhanced the 8-12 Hz coherence between ipsilateral S1/M1 and posterior parietal cortex. Early post-stimulus decrease of oscillatory coupling between S1/M1 and premotor cortex and between S1/M1 and medial frontal cortex suggests that these cortical regions act rather independently during processing of somatosensory information, and synchronize only later when the band power in contralateral S1/M1 increases. Motor cortex activation associated with ipsilateral hand muscle contraction interferes with cortical processing of somatosensory stimuli in S1/M1 cortices.